
"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class   _UpperCAmelCase				(		_lowerCAmelCase				):
      __SCREAMING_SNAKE_CASE :				Tuple				   =						"beit"




      def __init__(				self ,							lowercase_=8_1_9_2 ,							lowercase_=7_6_8 ,							lowercase_=1_2 ,							lowercase_=1_2 ,							lowercase_=3_0_7_2 ,							lowercase_="gelu" ,							lowercase_=0.0 ,							lowercase_=0.0 ,							lowercase_=0.0_2 ,							lowercase_=1E-12 ,							lowercase_=2_2_4 ,							lowercase_=1_6 ,							lowercase_=3 ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=True ,							lowercase_=[3, 5, 7, 1_1] ,							lowercase_=[1, 2, 3, 6] ,							lowercase_=True ,							lowercase_=0.4 ,							lowercase_=2_5_6 ,							lowercase_=1 ,							lowercase_=False ,							lowercase_=2_5_5 ,							**lowercase_ ,							)    ->       List[Any]:
          super().__init__(**SCREAMING_SNAKE_CASE_				)

          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       initializer_range
          UpperCAmelCase          =       layer_norm_eps

          UpperCAmelCase          =       image_size
          UpperCAmelCase          =       patch_size
          UpperCAmelCase          =       num_channels
          UpperCAmelCase          =       use_mask_token
          UpperCAmelCase          =       use_absolute_position_embeddings
          UpperCAmelCase          =       use_relative_position_bias
          UpperCAmelCase          =       use_shared_relative_position_bias
          UpperCAmelCase          =       layer_scale_init_value
          UpperCAmelCase          =       drop_path_rate
          UpperCAmelCase          =       use_mean_pooling
          # decode head attributes (semantic segmentation)
          UpperCAmelCase          =       out_indices
          UpperCAmelCase          =       pool_scales
          # auxiliary head attributes (semantic segmentation)
          UpperCAmelCase          =       use_auxiliary_head
          UpperCAmelCase          =       auxiliary_loss_weight
          UpperCAmelCase          =       auxiliary_channels
          UpperCAmelCase          =       auxiliary_num_convs
          UpperCAmelCase          =       auxiliary_concat_input
          UpperCAmelCase          =       semantic_loss_ignore_index

class   _UpperCAmelCase				(		_lowerCAmelCase				):
      __SCREAMING_SNAKE_CASE :				Any				   =						version.parse("1.11"				)




      @property
      def      a_   (				self				)    ->       Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
              ]				)




      @property
      def      a_   (				self				)    ->       float:
          return 1E-4








def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			if num <= 0:
						raise ValueError("""Input must be a positive integer"""  )

			UpperCAmelCase_:		Tuple							      =     [True] * (num + 1)

			UpperCAmelCase_:		Tuple							      =     2
			while p * p <= num:
						if primes[p]:
									for i in range(p * p		,						num + 1		,						lowerCAmelCase__  ):
												UpperCAmelCase_:		Optional[Any]							      =     False
						p += 1

			return [prime for prime in range(2		,						num + 1  ) if primes[prime]]


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	a      :  Optional[Any]			     =			int(input('Enter a positive integer: ').strip())
	print(prime_sieve_eratosthenes(user_num))






